Online Tracking of Linear Subspaces

We address the problem of online de-noising a stream of input points. We assume that the clean data is embedded in a linear subspace. We present two online algorithms for tracking subspaces and, as a consequence, de-noising. We also describe two regularization schemas which improve the resistance to noise. We analyze the algorithms in the loss bound model, and specify some of their properties. Preliminary simulations illustrate the usefulness of our algorithms.

• Type: Conference
• Year: 2006
• Where: COLT
• Authors: Koby Crammer
• Keywords: COLT 2006, Linear Subspace, Loss Bound Model, Machine Learning, Online De-noising